1. Field of the Invention
The present invention is a method and system for forecasting the behavioral characterization of customers to help customize programming contents on each node, defined as means for playing output, of each site of a plurality of sites in a media network through automatically measuring, characterizing, and forecasting the behavioral information of customers that appear in the vicinity of each means for playing output, using a plurality of image capturing devices and a plurality of computer vision technologies on the visual information, and the present invention is called behavior-based programming (BBP).
2. Background of the Invention
There have been earlier attempts to help customers and salespersons in a shopping process utilizing computer-aided devices, such as U.S. Pat. No. 5,047,614 of Bianco, U.S. Pat. No. 5,283,731 of Lalonde, et al. (hereinafter Lalonde), and U.S. Pat. No. 5,309,355 of Lockwood. Bianco disclosed a portable and remote bar code reading means. Lalonde disclosed a computer-based classified advertisement system. Lockwood disclosed an automated sales system, which enhances a travel agent's marketing ability; especially with regard to computerized airline reservation systems.
There have also been attempts to customize and distribute targeted advertising content to customers or television viewers based on customer profiles, customer purchase history, or demographic information from the customer in the prior art.
U.S. Pat. No. 5,155,591 of Wachob and U.S. Pat. No. 5,636,346 of Saxe disclosed methods and systems for delivering targeted advertisements and programming to demographically targeted television audiences. U.S. Pat. No. 6,002,393 of Hite, et al. disclosed a system and method for delivering targeted TV advertisements to customers utilizing controllers.
U.S. Pat. No. 5,459,306 of Stein, et al. (hereinafter Stein) disclosed a method and system for delivering product picks to a prospective individual user, especially with regard to a movie rental and sale business. Stein gathered the user information and the user's usage information, which are correlated with a user code and classified based on the use of at least one product. The product picks (promotions and recommendations) were delivered based on the classified information and the user information. However, Stein is foreign to the automatic method of gathering the user information, especially the user behavior, in a store.
U.S. Pat. No. 6,119,098 of Guyot, et al. (hereinafter Guyot) disclosed a method and apparatus for targeting and distributing advertisements over a distributed network, such as the Internet, to the subscriber's computer. The targeted advertisements were based on a personal profile provided by the subscriber. Guyot was primarily intended for the subscriber with a computer at home, not at a physical space, such as a retail place, and the targeted advertisement creation relied on the non-automatic response from the customer.
U.S. Pat. No. 6,182,050 of Ballard disclosed a method and apparatus for distributing advertisements online using target criteria screening, which also provided a method for maintaining end user privacy. In the disclosure, the demographic information or a desired affinity ranking was gathered by the end user, who completed a demographic questionnaire and ranked various categories of products and services. Ballard is foreign to the behavior analysis of customers in a retail store.
U.S. Pat. No. 6,055,573 of Gardenswartz, et al. and its continuation U.S. Pat. No. 6,298,330 of Gardenswartz, et al. (hereinafter Gardenswartz) disclosed a method and apparatus for communicating with a computer in a network based on the offline purchase history of a particular customer. Gardenswartz included the delivery of a promotional incentive for a customer to comply with a particular behavioral pattern. However, in Gardenswartz, the customer manually supplied the registration server with information about the customer, including demographics of the customer, to generate an online profile. In Gardenswartz, the content of advertisements were selected based on changes in the customers' purchase history behaviors, but Gardenswartz is foreign to the automatic behavioral pattern analysis using customer images and computer vision algorithms in a retail store, such as the shopping path analysis of the customers in the retail store. Furthermore, Gardenswartz is foreign to the concept of forecasting the customer behavioral pattern to help customize programming content in a media network.
U.S. Pat. No. 6,385,592 of Angles, et al. (hereinafter Angles) disclosed a method and apparatus for delivering customized advertisements within interactive communication systems. In Angles, the interactive devices include computers connected to online services, interactive kiosks, interactive television systems and the like. In Angles, the advertising provider computer generated a customized advertisement based on the customer's profile, upon receiving the advertising request. In Angles, the customer, who wished to receive customized advertisement, first registered with the advertisement provider by entering the demographic information into the advertisement provider's demographic database. Therefore, Angles is foreign to the automatic forecasting of customers' behavioral pattern for the programming in a retail space based on customer behavior, without requiring any cumbersome response from the customer.
U.S. Pat. No. 6,408,278 of Carney, et al. (hereinafter Carney) disclosed a method and apparatus for delivering programming content on a network of electronic out-of-home display devices. In Carney, the network includes a plurality of display devices located in public places, and the delivered programming content is changed according to the demographics of the people. Carney also suggests demographic data gathering devices, such as kiosk and automatic teller machines. Carney is foreign to the idea of forecasting customers' behavioral patterns for the programming based on the automatic analysis of the customer's behaviors inside the store utilizing non-cumbersome automatic computer vision technology.
U.S. Pat. No. 6,484,148 of Boyd disclosed electronic advertising devices and methods for providing targeted advertisements based on the customer profiles. Boyd included a receiver for receiving identifying signals from individuals such as signals emitted by cellular telephones, and the identifying signal was used for the targeted advertisements to be delivered to the individuals. U.S. Pat. No. 6,847,969 of Mathai, et al. (hereinafter Mathai) disclosed a method and system for providing personalized advertisements to customers in a public place. In Mathai, the customer inserts a personal system access card into a slot on a terminal, which automatically updates the customer profile based on the customer's usage history. The customer profile is used for targeted advertising in Mathai. However, the usage of a system access card is cumbersome to the customer. The customer has to carry around the card when shopping, and the method and apparatus is not usable if the card is lost or stolen. U.S. Pat. No. 6,529,940 of Humble also disclosed a method and system for interactive in-store marketing, using interactive display terminals that allow customers to input feedback information to the distributed marketing messages.
Boyd, Mathai, and Humble are foreign to the idea of forecasting customers' behavioral patterns for the programming content in a media network based on the automatic analysis of the customers' behaviors inside the store utilizing non-cumbersome automatic computer vision technology.
U.S. Pat. Appl. Pub. No. 2006/0036485 of Duri, et al. (hereinafter Duri) disclosed a method and system for presenting personalized information to consumers in a retail environment using the RFID technology. Duri very briefly mentioned the computer vision techniques as a method to locate each customer, but Duri is clearly foreign to the concept of utilizing an image processing algorithm in the computer vision technologies to gather behavior analysis information of the customers to customize the programming contents in a media network.
There have been earlier attempts for understanding customers' shopping behaviors captured in a video in a targeted environment, such as in a retail store, using cameras.
U.S. Pat. Appl. Pub. No. 2006/0010028 of Sorensen (hereinafter Sorensen 1) disclosed a method for tracking shopper movements and behavior in a shopping environment using a video. In Sorensen 1, a user indicated a series of screen locations in a display at which the shopper appeared in the video, and the series of screen locations was translated to store map coordinates. The step of receiving the user input via input devices, such as a pointing device or keyboard, makes Sorensen 1 inefficient for handling a large amount of video data in a large shopping environment with a relatively complicated store layout, especially over a long period of time. The manual input by a human operator/user cannot efficiently track all of the shoppers in such cases, not to mention the possibility of human errors due to tiredness and boredom. Additionally, the manual input approach is not scalable when the number of shopping environments to handle increases.
Although U.S. Pat. Appl. Pub. No. 2002/0178085 of Sorensen (hereinafter Sorensen 2) disclosed a usage of tracking device and store sensors in a plurality of tracking systems primarily based on the wireless technology, such as the RFID. Sorensen 2 is clearly foreign to the concept of applying computer vision based tracking algorithms to the field of understanding customers' shopping behaviors and movements. In Sorensen 2, each transmitter was typically attached to a hand-held or push-type cart. Therefore, Sorensen 2 cannot distinguish the behaviors of multiple shoppers using one cart from a single shopper who is also using one cart. Although Sorensen 2 disclosed that the transmitter may be attached directly to a shopper via a clip or other form of customer surrogate when a customer is shopping without a cart, this will not be practical due to the additionally introduced cumbersome steps to the shopper, not to mention the inefficiency of managing the transmitter for each individual shopper.
With regard to the temporal behavior of customers, U.S. Pat. Appl. Pub. No. 2003/0002712 of Steenburgh, et al. (hereinafter Steenburgh) disclosed a relevant prior art. Steenburgh disclosed a method for measuring dwell time of an object, particularly a customer in a retail store, which enters and exits an environment, by tracking the object and matching the entry signature of the object to the exit signature of the object, in order to find out how long the customer spent in a retail store.
U.S. Pat. Appl. Pub. No. 2003/0053659 of Pavlidis, et al. (hereinafter Pavlidis) disclosed a method for moving object assessment, including an object path of one or more moving objects in a search area, using a plurality of imaging devices and segmentation by background subtraction. In Pavlidis, the object included customers. Pavlidis was primarily related to monitoring a search area for surveillance, but Pavlidis also included itinerary statistics of customers in a department store.
U.S. Pat. Appl. Pub. No. 2004/0120581 of Ozer, et al. (hereinafter Ozer) disclosed a method for identifying activity of customers for marketing purpose or activity of objects in a surveillance area, by comparing the detected objects with the graphs from a database. Ozer tracked the movement of different object parts and combined them to high-level activity semantics, using several Hidden Markov Models (HMMs) and a distance classifier. U.S. Pat. Appl. Pub. No. 2004/0131254 of Liang, et al. (hereinafter Liang) also disclosed the Hidden Markov Models (HMMs) as a way to characterize behavior, particularly animal behavior, along with the rule-based label analysis and the token parsing procedure. Liang disclosed a method for monitoring and classifying actions of various objects in a video, using background subtraction for object detection and tracking. Liang is particularly related to animal behavior in a lab for testing drugs.
With regard to path analysis, an exemplary disclosure can be found in U.S. Pat. No. 6,584,401 of Kirshenbaum, et al. (hereinafter Kirshenbaum), which disclosed a method and apparatus for automatically gathering data on paths taken by commuters for the sake of improving the commute experience. Kirshenbaum disclosed a global positioning system, mobile phone, personal digital assistant, telephone, PC, and departure or arrival indications as some ways for gathering the commute data. Clearly, Kirshenbaum is foreign to the concept of analyzing the customers' behaviors automatically based on visual information of the customers using the means for capturing images, such as the shopping path tracking and analysis, for the sake of delivering targeted advertisement content to a display in a retail store.
U.S. Pat. Appl. Pub. No. 2003/0058339 of Trajkovic, et al. (hereinafter Trajkovic) disclosed a method for detecting an event through repetitive patterns of human behavior. Trajkovic learned multidimensional feature data from the repetitive patterns of human behavior and computed a probability density function (PDF) from the data. Then, a method for the PDF analysis, such as Gaussian or clustering techniques, was used to identify the repetitive patterns of behavior and unusual behavior through the variance of the Gaussian distribution or cluster.
Although Trajkovic can model a repetitive behavior through the PDF analysis, Trajkovic is clearly foreign to the event detection for the aggregate of non-repetitive behaviors, such as the shopper traffic in a physical store. The shopping path of an individual shopper can be repetitive, but each shopping path in a group of aggregated shopping paths of multiple shoppers is not repetitive. Trajkovic did not disclose the challenges in the event detection based on customers' behaviors in a video in a retail environment such as the non-repetitive behaviors, and Trajkovic is clearly foreign to the challenges that can be found in a retail environment.
While the above mentioned prior arts try to deliver targeted advertising contents to the customers in a computer network, television network, or a standalone system, using customer profiles, customer purchase history, demographic information from customers, various devices and tools, or non-automatic information collection methods, such as questionnaires, registration forms, or electronic devices from the customers, they are clearly foreign to the automatic forecasting of customers' behavioral patterns in a retail space based on the customers' behavioral statistics and classification, such as the shopping paths information in the store, without requiring any cumbersome involvement from the customer, using an efficient computer vision technology on the customers' images.
In the present invention, the term “programming” is defined as any media content that is delivered to the sites in a particular media network, including any advertisement, public announcement, informational message, promotional content, marketing content, and educational content. Therefore, the term programming in the present invention includes a much broader concept of content than a mere advertisement content. In this context, the prior arts are especially foreign to the concept of providing forecasting information to help customize the programming content, rather than just advertisement content, in a media network based on automatic behavior analysis by computer vision algorithms.
The present invention is a method and system for forecasting the behavioral characterization of customers to help customize programming contents on each node, defined as means for playing output, of each site of a plurality of sites in a media network through automatically measuring, characterizing, and forecasting the behavioral information of customers that appear in the vicinity of each means for playing output, using a plurality of image capturing devices and a plurality of computer vision technologies on the visual information, which solves the aforementioned problems in the prior art. It is an objective of the present invention to provide an efficient and robust solution that solves the aforementioned problems in the prior art. The present invention is called behavior-based programming (BBP).
Computer vision algorithms have been shown to be an effective means for detecting and tracking people. These algorithms also have been shown to be effective in analyzing the behavior of people in the view of the means for capturing images. This allows for the possibility of connecting the visual information from a scene to the behavior and content of advertising media. The invention allows freedom of installation position between data gathering devices, a set of cameras, and display devices. The invention automatically and unobtrusively analyzes the customer behavior without involving any hassle of feeding information manually by the customer. The present invention does not require the customer to carry any cumbersome device.
Another limitation found in the prior arts is that the data gathering device is often collocated adjacent to the display device in the prior art. However, depending on the public place environment and the business goal, where the embodiment of the system is installed, it may be necessary to install the data gathering devices independent of the position of the display device. For example, some owners of public places could want to utilize the widely used and already installed surveillance cameras in their public places for the data gathering. In this situation, the surveillance cameras may not necessarily be collocated adjacent to the display devices.
The BBP enables the separation of the device locations, which makes the layout of equipment installation flexible. In the above exemplary cases, the BBP enables the targeted content to be delivered and displayed through display devices, which do not need to be collocated adjacent to the data gathering devices, such as cameras.